计算机工程与应用2025，Vol.61Issue(11)：316-324,9.DOI:10.3778/j.issn.1002-8331.2403-0126
融合多尺度特征的高效网片缺陷检测算法
Efficient Mesh Defect Detection Algorithm Integrating Multi-Scale Features
摘要
Abstract
Addressing the inefficiency of manual visual inspection for mesh defects in large rotary filtration equipment,and considering the blurred boundaries between the defects and the background,and the reflection phenomenon caused by the small water droplets in the mesh holes,this paper proposes an efficient mesh defect detection algorithm based on multi-dimensional feature fusion.Firstly,the Poisson image enhancement technique is applied to achieve seamless fusion between defect targets and normal background areas thereby enhancing the visual coherence of the composite image.This approach effectively addresses the issue of uneven distribution of defect numbers by increasing the representation of small sample defects.Next,the paper integrates the lightweight multi-dimensional convolutional improved C2f_LWDC(C2f_lightweight multi-dimensional convolution)module and the weighted multi-feature enhancement module into YOLOv8.This integration not only enhances the extraction of defect features but also achieves efficient fusion of features at all levels.As a result,it improves the characterization ability of multi-scale defect samples.Lastly,the EIOU(expected intersection over union)localization loss function is employed to expedite the precise localization of the defect target.The detection results of mesh dataset show that the improved algorithm mAP(mean average precision)reaches 92%,which is 16.8 percentage points higher than that of the original model,and the improved algorithm can well complete the detection task of defect targets.关键词
网片缺陷/YOLOv8/轻量多维卷积/特征融合/多尺度Key words
mesh defect/YOLOv8/lightweight multidimensional convolution/feature fusion/multiscale分类
信息技术与安全科学引用本文复制引用
何钢,姚远,韩征彤,邹华涛,王田田..融合多尺度特征的高效网片缺陷检测算法[J].计算机工程与应用,2025,61(11):316-324,9.基金项目
国家自然科学基金(52175223). （52175223）
